Overview

Dataset statistics

Number of variables13
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.0 KiB
Average record size in memory100.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 4 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with qtde_returns and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_basket_sizeHigh correlation
qtde_returns is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.15706781) Skewed
frequency is highly skewed (γ1 = 24.87675009) Skewed
qtde_returns is highly skewed (γ1 = 21.9754032) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-11-27 15:18:41.176444
Analysis finished2022-11-27 15:20:17.586306
Duration1 minute and 36.41 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2316.666442
Minimum0
Maximum5714
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:17.885777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.35
Q1928.5
median2119.5
Q33536.25
95-th percentile5034.3
Maximum5714
Range5714
Interquartile range (IQR)2607.75

Descriptive statistics

Standard deviation1554.722712
Coefficient of variation (CV)0.6711033938
Kurtosis-1.010637904
Mean2316.666442
Median Absolute Deviation (MAD)1270.5
Skewness0.3426249769
Sum6875866
Variance2417162.71
MonotonicityStrictly increasing
2022-11-27T12:20:18.327559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30101
 
< 0.1%
29951
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57141
< 0.1%
56951
< 0.1%
56851
< 0.1%
56791
< 0.1%
56581
< 0.1%
56541
< 0.1%
56481
< 0.1%
56371
< 0.1%
56361
< 0.1%
56261
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.37702
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2022-11-27T12:20:18.981360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.144523
Coefficient of variation (CV)0.1125803587
Kurtosis-1.206178196
Mean15270.37702
Median Absolute Deviation (MAD)1489
Skewness0.03219371129
Sum45322479
Variance2955457.892
MonotonicityNot monotonic
2022-11-27T12:20:19.476629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
126701
 
< 0.1%
177341
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
Other values (2958)2958
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.389373
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:19.933004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.32607
Coefficient of variation (CV)3.763037818
Kurtosis397.3184084
Mean2693.389373
Median Absolute Deviation (MAD)671.39
Skewness17.63574461
Sum7993979.66
Variance102724834.5
MonotonicityNot monotonic
2022-11-27T12:20:20.352746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.962
 
0.1%
2053.022
 
0.1%
3312
 
0.1%
1353.742
 
0.1%
889.932
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
734.942
 
0.1%
Other values (2943)2948
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140438.721
< 0.1%
124564.531
< 0.1%
117375.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65019.621
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.31030997
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:20.785479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.76031378
Coefficient of variation (CV)1.209142264
Kurtosis2.77659321
Mean64.31030997
Median Absolute Deviation (MAD)26
Skewness1.79807024
Sum190873
Variance6046.666399
MonotonicityNot monotonic
2022-11-27T12:20:21.222210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
2255
 
1.9%
Other values (262)2218
74.7%
ValueCountFrequency (%)
033
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724056604
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:21.718562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.857882575
Coefficient of variation (CV)1.5474834
Kurtosis190.7771511
Mean5.724056604
Median Absolute Deviation (MAD)2
Skewness10.76520644
Sum16989
Variance78.46208371
MonotonicityNot monotonic
2022-11-27T12:20:22.138794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.4%
3498
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2785
26.4%
3498
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1664
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1579.712264
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:22.573618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.35
Q1296
median638
Q31398.25
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1102.25

Descriptive statistics

Standard deviation5700.529956
Coefficient of variation (CV)3.608587516
Kurtosis518.1228414
Mean1579.712264
Median Absolute Deviation (MAD)419
Skewness18.7602581
Sum4688586
Variance32496041.78
MonotonicityNot monotonic
2022-11-27T12:20:23.005320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2608
 
0.3%
848
 
0.3%
2888
 
0.3%
2728
 
0.3%
2468
 
0.3%
5167
 
0.2%
3947
 
0.2%
Other values (1654)2885
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
799631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577851
< 0.1%
502551
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7456199
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:23.484460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8785162
Coefficient of variation (CV)2.198681439
Kurtosis354.7550751
Mean122.7456199
Median Absolute Deviation (MAD)44
Skewness15.70464041
Sum364309
Variance72834.41353
MonotonicityNot monotonic
2022-11-27T12:20:23.908768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2845
 
1.5%
2038
 
1.3%
3535
 
1.2%
1533
 
1.1%
2933
 
1.1%
1933
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2529
 
1.0%
Other values (459)2629
88.6%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
315
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
927
0.9%
1027
0.9%
ValueCountFrequency (%)
78371
< 0.1%
56701
< 0.1%
50951
< 0.1%
45771
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16361
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.99655282
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:24.381592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.915887985
Q113.11811111
median17.96548505
Q324.98179365
95-th percentile90.052125
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.86368254

Descriptive statistics

Standard deviation119.5318165
Coefficient of variation (CV)3.622554671
Kurtosis812.969606
Mean32.99655282
Median Absolute Deviation (MAD)5.980669355
Skewness25.15706781
Sum97933.76878
Variance14287.85517
MonotonicityNot monotonic
2022-11-27T12:20:24.858439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2955)2955
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.30505288
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:25.276285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.9271978
median48.26785714
Q385.33333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.40613553

Descriptive statistics

Standard deviation63.50325927
Coefficient of variation (CV)0.9435139941
Kurtosis4.908645262
Mean67.30505288
Median Absolute Deviation (MAD)26.26785714
Skewness2.06622239
Sum199761.397
Variance4032.663938
MonotonicityNot monotonic
2022-11-27T12:20:25.691529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
1117
 
0.6%
4617
 
0.6%
2117
 
0.6%
2816
 
0.5%
Other values (1248)2776
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1138262908
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:26.124581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008893504781
Q10.01633986928
median0.02589835169
Q30.04942659085
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03308672157

Descriptive statistics

Standard deviation0.4082214549
Coefficient of variation (CV)3.586354717
Kurtosis989.0590635
Mean0.1138262908
Median Absolute Deviation (MAD)0.0121968864
Skewness24.87675009
Sum337.8364311
Variance0.1666447562
MonotonicityNot monotonic
2022-11-27T12:20:26.545563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.0277777777817
 
0.6%
0.062517
 
0.6%
0.0238095238116
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0769230769213
 
0.4%
Other values (1215)2636
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.88847709
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:27.009689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.864784
Coefficient of variation (CV)8.107685048
Kurtosis596.2019916
Mean34.88847709
Median Absolute Deviation (MAD)1
Skewness21.9754032
Sum103549
Variance80012.48604
MonotonicityNot monotonic
2022-11-27T12:20:27.410447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (203)705
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1972
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.7885065
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:27.854174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172
Q3281.375
95-th percentile598.345
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.1375

Descriptive statistics

Standard deviation283.7237528
Coefficient of variation (CV)1.203297637
Kurtosis103.0742725
Mean235.7885065
Median Absolute Deviation (MAD)82.625
Skewness7.717538936
Sum699820.2873
Variance80499.16789
MonotonicityNot monotonic
2022-11-27T12:20:28.282591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
869
 
0.3%
829
 
0.3%
888
 
0.3%
758
 
0.3%
608
 
0.3%
1368
 
0.3%
1307
 
0.2%
Other values (1962)2881
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct910
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.49039145
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-27T12:20:28.715572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.03571429
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.36904762

Descriptive statistics

Standard deviation15.4620774
Coefficient of variation (CV)0.8840326672
Kurtosis29.30304319
Mean17.49039145
Median Absolute Deviation (MAD)6.6
Skewness3.434441407
Sum51911.48183
Variance239.0758377
MonotonicityNot monotonic
2022-11-27T12:20:29.151586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1343
 
1.4%
942
 
1.4%
1641
 
1.4%
839
 
1.3%
1737
 
1.2%
1437
 
1.2%
736
 
1.2%
1136
 
1.2%
534
 
1.1%
1534
 
1.1%
Other values (900)2589
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2591
< 0.1%
1771
< 0.1%
1481
< 0.1%
1271
< 0.1%
1051
< 0.1%
1041
< 0.1%
1011
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%

Interactions

2022-11-27T12:20:10.929557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:57.273170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:01.989494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:06.665014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:27.659379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:32.945060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:37.425929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:42.399291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:47.315893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:51.804364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:56.668488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:01.504805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:06.080244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:11.271343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:57.654159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:02.337174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:07.000245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:28.179345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:33.262982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:37.810733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:42.758070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:47.652474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:52.158147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:57.025285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:01.837600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:06.429030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:11.632358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:58.021620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:02.688260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:07.346033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:28.590847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:33.613188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:38.186618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:43.138237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:47.985483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:52.526918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:57.393583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:02.182388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:06.793807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:11.994133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:58.380400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:03.026291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:07.686029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:28.892660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:33.949174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:38.543582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:43.499456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:48.331396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:52.873714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:57.758451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:02.526175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:07.149585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:12.366904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:58.740098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:03.407129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:08.064780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:29.274918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:34.300377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:38.955518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:43.889637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:48.722657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:53.250276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:58.131066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:02.873963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:07.517359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:12.684713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:59.083326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:03.740754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:08.382400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:29.631716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:34.614565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:39.308465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:44.252454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:49.052454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:53.599089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:58.474854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:03.207756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:07.867143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:13.079195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:59.469380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:04.129127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:08.781431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:30.066141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:34.995993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:39.708345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:44.663924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:49.417230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:54.000375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:58.857430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:03.587521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:08.269261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:13.474955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:18:59.845036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:04.527021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:09.183391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:30.483902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:35.376323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:40.129615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:45.072835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:49.782004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:54.411145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:59.261346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:03.993931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:08.671423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:13.844733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:00.182961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:04.860816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:09.520632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:30.850718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:35.697111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:40.482766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:45.431847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:50.106836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:54.757305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:59.589615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:04.313736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:09.074294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:14.215536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:00.581354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:05.237307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:09.914519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:31.240476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:36.054038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:40.875637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:45.827072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:50.457408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:55.137071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:59.977060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:04.674513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:09.538354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:14.584316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:00.957121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:05.620428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:10.309350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:31.629183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:36.409134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:41.278104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:46.213275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:50.823183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:55.551824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:00.382838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:05.055278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:09.910105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:14.915125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:01.286919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:05.959208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:10.624882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:32.196484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:36.733943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:41.644756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:46.560147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:51.135777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:55.898626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:00.749416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:05.378079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:10.236868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:15.274926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:01.648968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:06.312989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:11.001231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:32.576280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:37.087138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:42.038514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:46.947345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:51.470570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:19:56.292297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:01.160018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:05.743816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-27T12:20:10.587654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-27T12:20:29.566393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-27T12:20:30.168756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-27T12:20:30.768387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-27T12:20:31.382009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-27T12:20:31.971646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-27T12:20:15.867092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-27T12:20:17.206360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000017.00000040.050.9705880.617647
11130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444411.666667
22125836705.382.015.05028.0232.028.90250023.1875000.04032350.0335.2000007.600000
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666670.333333
55152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428574.357143
66146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.4285717.047619
77178095411.9116.012.02057.061.088.71983635.7000000.03352041.0171.4166673.833333
881531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.7142866.230769
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29585626177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.000000
2959563617232421.522.02.0203.036.011.70888912.00.1538460.0101.50000015.000000
2960563717468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.500000
2961564813596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000066.500000
29625654148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.000000
2963565812479473.2011.01.0382.030.015.7733334.01.00000034.0382.00000030.000000
2964567914126706.137.03.0508.015.047.0753333.00.75000050.0169.3333334.666667
29655685135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333104.000000
2966569515060301.848.04.0262.0120.02.5153331.02.0000000.065.50000020.000000
2967571412558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.000000